Hecht David, Cheung Mars, Fogel Gary B
Southwestern College, 900 Otay Lakes Road, Chula Vista, CA 91910 USA.
Biosystems. 2008 Apr;92(1):10-5. doi: 10.1016/j.biosystems.2007.10.005. Epub 2007 Nov 17.
Quantitative structure-activity relationship (QSAR) models were developed for dihydrofolate reductase (DHFR) inhibition by pyrimethamine derivatives using small molecule descriptors derived from MOE and/or QikProp and linear or nonlinear modeling. During this analysis, the best QSAR models were identified when using MOE descriptors and nonlinear models (artificial neural networks) optimized by evolutionary computation. The resulting models can be used to identify key descriptors for DHFR inhibition and are useful for high-throughput screening of novel drug leads.
利用源自MOE和/或QikProp的小分子描述符以及线性或非线性建模方法，建立了关于乙胺嘧啶衍生物对二氢叶酸还原酶（DHFR）抑制作用的定量构效关系（QSAR）模型。在该分析过程中，使用MOE描述符和通过进化计算优化的非线性模型（人工神经网络）时，确定了最佳的QSAR模型。所得模型可用于识别DHFR抑制的关键描述符，有助于新型药物先导物的高通量筛选。
